-
1
-
-
77958488310
-
Deep machine learning-A new frontier in artificial intelligence research
-
Arel, I., D.C. Rose, and T.P. Karnowski, 2010. Deep machine learning-A new frontier in artificial intelligence research, IEEE Computational Intelligence Magazine, 5(4):13–18.
-
(2010)
IEEE Computational Intelligence Magazine
, vol.5
, Issue.4
, pp. 13-18
-
-
Arel, I.1
Rose, D.C.2
Karnowski, T.P.3
-
2
-
-
69349090197
-
-
Now Publishers, Inc
-
Bengio, Y., 2009. Learning deep architectures for AI, Foundations and Trends in Machine Learning, Now Publishers, Inc., 2(1):1–127.
-
(2009)
Learning Deep Architectures for AI, Foundations and Trends in Machine Learning
, vol.2
, Issue.1
, pp. 1-127
-
-
Bengio, Y.1
-
3
-
-
84864073449
-
Greedy layer-wise training of deep networks
-
(NIPS)
-
Bengio, Y., P. Lamblin, D. Popovici and H. Larochelle, 2007. Greedy layer-wise training of deep networks. Proceedings of Advances in Neural Information Processing Systems 19 (NIPS), pp. 153–160.
-
(2007)
Proceedings of Advances in Neural Information Processing Systems
, vol.19
, pp. 153-160
-
-
Bengio, Y.1
Lamblin, P.2
Popovici, D.3
Larochelle, H.4
-
4
-
-
84879854889
-
Representation learning: A review and new perspectives
-
Bengio, Y., A. Courville, and P. Vincent, 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35:1798–1828.
-
(2013)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.35
, pp. 1798-1828
-
-
Bengio, Y.1
Courville, A.2
Vincent, P.3
-
5
-
-
77955993281
-
Learning mid-level features for recognition
-
13-18 June, San Francisco, California
-
Boureau, Y.-L, F. Bach, Y. LeCun, and J. Ponce, 2010. Learning mid-level features for recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June, San Francisco, California, pp. 2559–2566.
-
(2010)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
, pp. 2559-2566
-
-
Boureau, Y.-L.1
Bach, F.2
Lecun, Y.3
Ponce, J.4
-
6
-
-
85032751634
-
Advances in hyperspectral image classification: Earth monitoring with statistical learning methods
-
Camps-Valls, G., D. Tuia, L. Bruzzone, and J.A. Benediktsson, 2014. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods, IEEE Signal Processing Magazine, 31(1):45–54.
-
(2014)
IEEE Signal Processing Magazine
, vol.31
, Issue.1
, pp. 45-54
-
-
Camps-Valls, G.1
Tuia, D.2
Bruzzone, L.3
Benediktsson, J.A.4
-
8
-
-
84905925092
-
Deep Learningbased classification of hyperspectral data
-
Chen, Y., Zh. Lin, X. Zhao, G. Wang, and Y. Gu, 2014. Deep Learningbased classification of hyperspectral data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2094–2107.
-
(2014)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, vol.7
, Issue.6
, pp. 2094-2107
-
-
Chen, Y.1
Lin, Z.H.2
Zhao, X.3
Wang, G.4
Gu, Y.5
-
9
-
-
85027942618
-
Spectral-spatial classification of hyperspectral data based on deep belief network
-
Chen, Y., X. Zhao, and X. Jia, 2015. Spectral-spatial classification of hyperspectral data based on deep belief network, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6):2381–2392.
-
(2015)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, vol.8
, Issue.6
, pp. 2381-2392
-
-
Chen, Y.1
Zhao, X.2
Jia, X.3
-
10
-
-
0024856071
-
Application of remote sensing and geographic information systems to forest fire hazard mapping
-
Chuvieco, E., and R.G. Congalton, 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping, Remote Sensing of Environment, 29(2):147–159.
-
(1989)
Remote Sensing of Environment
, vol.29
, Issue.2
, pp. 147-159
-
-
Chuvieco, E.1
Congalton, R.G.2
-
11
-
-
84862283411
-
An analysis of single-layer networks in unsupervised feature learning
-
Coates, A., A.Y. Ng, and H. Lee, 2011. An analysis of single-layer networks in unsupervised feature learning, Journal of Machine Learning Research, 15:215–223.
-
(2011)
Journal of Machine Learning Research
, vol.15
, pp. 215-223
-
-
Coates, A.1
Ng, A.Y.2
Lee, H.3
-
12
-
-
0026278621
-
A review of assessing the accuracy of classification of remotely sensed data
-
Congalton, R.G., 1991. A review of assessing the accuracy of classification of remotely sensed data, Remote Sensing of Environment, 37(1):35–46.
-
(1991)
Remote Sensing of Environment
, vol.37
, Issue.1
, pp. 35-46
-
-
Congalton, R.G.1
-
14
-
-
34249753618
-
Support Vector Networks
-
Cortes, C., and V. Vapnik, 1995. Support Vector Networks, Machine Learning, 20:273–297.
-
(1995)
Machine Learning
, vol.20
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
15
-
-
56849127860
-
Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles
-
Fauvel, M., J.A. Benediktsson, J. Chanussot, and J.R. Sveinsson, 2008. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles, IEEE Transactions on Geoscience and Remote Sensing, 46(11):3804–3814.
-
(2008)
IEEE Transactions on Geoscience and Remote Sensing
, vol.46
, Issue.11
, pp. 3804-3814
-
-
Fauvel, M.1
Benediktsson, J.A.2
Chanussot, J.3
Sveinsson, J.R.4
-
16
-
-
84899967600
-
Advances in spectral-spatial classification of hyperspectral images
-
Fauvel, M., Y. Tarabalka, J.A. Benediktsson, J. Chanussot, and J.C. Tilton, 2013. Advances in spectral-spatial classification of hyperspectral images, Proceedings of the IEEE, 101(3):652–675.
-
(2013)
Proceedings of the IEEE
, vol.101
, Issue.3
, pp. 652-675
-
-
Fauvel, M.1
Tarabalka, Y.2
Benediktsson, J.A.3
Chanussot, J.4
Tilton, J.C.5
-
17
-
-
12844263684
-
Convolutional face finder: A neural architecture for fast and robust face detection
-
Garcia, C., and M. Delakis, 2004. Convolutional face finder: A neural architecture for fast and robust face detection, IEEE Pattern Analysis and Machine Intelligence, 26(11):1408–1423.
-
(2004)
IEEE Pattern Analysis and Machine Intelligence
, vol.26
, Issue.11
, pp. 1408-1423
-
-
Garcia, C.1
Delakis, M.2
-
18
-
-
84889465662
-
-
Wiley, Hoboken, New Jersey
-
Grahn, H.F., and P. Geladi, 2007. Techniques and Applications of Hyperspectral Image Analysis, Wiley, Hoboken, New Jersey.
-
(2007)
Techniques and Applications of Hyperspectral Image Analysis
-
-
Grahn, H.F.1
Geladi, P.2
-
19
-
-
84939247735
-
Spatial pyramid pooling in deep convolutional networks for visual recognition
-
He, K., X. Zhang, S. Ren, and J. Sun, 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Pattern Analysis and Machine Intelligence, 37(9):1904–1916.
-
(2015)
IEEE Pattern Analysis and Machine Intelligence
, vol.37
, Issue.9
, pp. 1904-1916
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
20
-
-
33745805403
-
A fast learning algorithm for deep belief nets
-
Hinton, G.E., S. Osindero, and Y.-W. Teh, 2006. A fast learning algorithm for deep belief nets, Neural Computation, 18(7):1527–1554.
-
(2006)
Neural Computation
, vol.18
, Issue.7
, pp. 1527-1554
-
-
Hinton, G.E.1
Osindero, S.2
Teh, Y.-W.3
-
21
-
-
33746600649
-
Reducing the dimensionality of data with neural networks
-
Hinton, G.E., and R.R. Salakhutdinov, 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786):504–507.
-
(2006)
Science
, vol.313
, Issue.5786
, pp. 504-507
-
-
Hinton, G.E.1
Salakhutdinov, R.R.2
-
22
-
-
84890466217
-
Improving neural networks by preventing co-adaptation of feature detectors
-
abs/1207.0580
-
Hinton, G.E., N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov, 2012. Improving neural networks by preventing co-adaptation of feature detectors, CoRR, abs/1207.0580.
-
(2012)
Corr
-
-
Hinton, G.E.1
Srivastava, N.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.R.5
-
23
-
-
84891559599
-
Spectral-spatial constraint hyperspectral image classification
-
Ji, R., Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li, 2014. Spectral-spatial constraint hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 52(3):1811–1824.
-
(2014)
IEEE Transactions on Geoscience and Remote Sensing
, vol.52
, Issue.3
, pp. 1811-1824
-
-
Ji, R.1
Gao, Y.2
Hong, R.3
Liu, Q.4
Tao, D.5
Li, X.6
-
24
-
-
16444366244
-
Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data
-
Jimenez, L.O., J.L. Rivera-Median, and E. Rodriguez-Diaz, 2005. Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, 43(4):844–851.
-
(2005)
IEEE Transactions on Geoscience and Remote Sensing
, vol.43
, Issue.4
, pp. 844-851
-
-
Jimenez, L.O.1
Rivera-Median, J.L.2
Rodriguez-Diaz, E.3
-
25
-
-
84896314121
-
Spectral-spatial hyperspectral image classification with edge-preserving filtering
-
Kang, X., Sh. Li, and J.A. Benediktsson, 2014. Spectral-spatial hyperspectral image classification with edge-preserving filtering, IEEE Transactions on Geoscience and Remote Sensing, 52(5):2666–2677.
-
(2014)
IEEE Transactions on Geoscience and Remote Sensing
, vol.52
, Issue.5
, pp. 2666-2677
-
-
Kang, X.1
Li, S.H.2
Benediktsson, J.A.3
-
26
-
-
84878919540
-
ImageNet classification with deep convolutional neural networks
-
Krizhevsky, A., I. Sutskever, and G.E. Hinton, 2012. ImageNet classification with deep convolutional neural networks, Proceedings of Advances in Neural Information Processing Systems (NIPS), 25:1090–1098.
-
(2012)
Proceedings of Advances in Neural Information Processing Systems (NIPS)
, vol.25
, pp. 1090-1098
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
28
-
-
84930630277
-
Deep learning
-
LeCun, Y., Y. Bengio, and G.E. Hinton, 2015. Deep learning, Nature, 521(7553):436–444.
-
(2015)
Nature
, vol.521
, Issue.7553
, pp. 436-444
-
-
Lecun, Y.1
Bengio, Y.2
Hinton, G.E.3
-
29
-
-
0032203257
-
Gradient based learning applied to document recognition
-
LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner, 1998. Gradient based learning applied to document recognition, Proceedings of the IEEE, 86(11):2278–2324.
-
(1998)
Proceedings of the IEEE
, vol.86
, Issue.11
, pp. 2278-2324
-
-
Lecun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
30
-
-
79953223828
-
-
Springer, New York
-
LeCun, Y., L. Bottou, G.B. Orr, and K.R. Müller, 1998. Efficient BackProp, Neural Networks: Tricks of the Trade, Springer, New York, pp. 9–50.
-
(1998)
Efficient Backprop, Neural Networks: Tricks of the Trade
, pp. 9-50
-
-
Lecun, Y.1
Bottou, L.2
Orr, G.B.3
Müller, K.R.4
-
31
-
-
84959245070
-
Learning deep representations for ground-to-aerial geolocalization
-
07-12 June, Boston Massachusetts
-
Lin, T., Y. Cui, S. Belongie, and J. Hays, 2015. Learning deep representations for ground-to-aerial geolocalization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 07-12 June, Boston Massachusetts, pp. 5007–5015.
-
(2015)
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
, pp. 5007-5015
-
-
Lin, T.1
Cui, Y.2
Belongie, S.3
Hays, J.4
-
32
-
-
33646887390
-
On the limited memory BFGS method for large scale optimization
-
Liu, D.C., and J. Nocedal, 1989. On the limited memory BFGS method for large scale optimization, Mathematical Programming, 45(1–3):503–528.
-
(1989)
Mathematical Programming
, vol.45
, Issue.1-3
, pp. 503-528
-
-
Liu, D.C.1
Nocedal, J.2
-
33
-
-
84883778115
-
Remote sensingbased house value estimation using an optimized regional regression model
-
Lu, Zh. Y., J. Im, L. Quackenbush, and S. Yoo, 2013. Remote sensingbased house value estimation using an optimized regional regression model. Photogrammetric Engineering & Remote Sensing, 12(7):809–820.
-
(2013)
Photogrammetric Engineering & Remote Sensing
, vol.12
, Issue.7
, pp. 809-820
-
-
Lu, Z.Y.1
Im, J.2
Quackenbush, L.3
Yoo, S.4
-
34
-
-
79955521136
-
Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis
-
Dalla Mura, M., A. Villa, J.A. Benediktsson, J. Chanussot, and L. Bruzzone, 2011. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis, IEEE Geoscience and Remote Sensing Letters, 8(3):542–546.
-
(2011)
IEEE Geoscience and Remote Sensing Letters
, vol.8
, Issue.3
, pp. 542-546
-
-
Dalla Mura, M.1
Villa, A.2
Benediktsson, J.A.3
Chanussot, J.4
Bruzzone, L.5
-
35
-
-
85018665540
-
Sparse autoencoder, CS294A Lecture Notes
-
Ng, A., 2010. Sparse autoencoder, CS294A Lecture Notes, Stanford University.
-
(2010)
Stanford University
-
-
Ng, A.1
-
36
-
-
77950918832
-
Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data
-
01-04 September, Grenoble, France
-
Plaza, A., J. Plaza, and G. Martin, 2009. Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data, Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 01-04 September, Grenoble, France, pp. 1–6.
-
(2009)
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing
, pp. 1-6
-
-
Plaza, A.1
Plaza, J.2
Martin, G.3
-
37
-
-
84907063378
-
A hierarchical building detection method for very high resolution remotely sensed images combined with DSM using graph cut optimization
-
Qin, R.J., and W. Fang, 2014. A hierarchical building detection method for very high resolution remotely sensed images combined with DSM using graph cut optimization, Photogrammetric Engineering & Remote Sensing, 11(7):873–883.
-
(2014)
Photogrammetric Engineering & Remote Sensing
, vol.11
, Issue.7
, pp. 873-883
-
-
Qin, R.J.1
Fang, W.2
-
38
-
-
84883754549
-
Hyperspectral remote sensing of vegetation and agricultural crops
-
Thenkabail, P.S., M.K. Gumma, P.G. Teluguntla, and M. Tlyas, 2015. Hyperspectral remote sensing of vegetation and agricultural crops, Photogrammetric Engineering & Remote Sensing, 80(4):697–709.
-
(2015)
Photogrammetric Engineering & Remote Sensing
, vol.80
, Issue.4
, pp. 697-709
-
-
Thenkabail, P.S.1
Gumma, M.K.2
Teluguntla, P.G.3
Tlyas, M.4
-
39
-
-
45749110924
-
Representational power of restricted Boltzmann Machines and deep belief networks
-
Roux, L., and Y. Bengio, 2008. Representational power of restricted Boltzmann Machines and deep belief networks, Neural Computation, 20(6):1631–1649.
-
(2008)
Neural Computation
, vol.20
, Issue.6
, pp. 1631-1649
-
-
Roux, L.1
Bengio, Y.2
-
40
-
-
84973503481
-
Unsupervised deep feature extraction for remote sensing image classification
-
Romero, A., C. Gatta, and G. Camps-Valls, 2015. Unsupervised deep feature extraction for remote sensing image classification, IEEE Transactions on Geoscience and Remote Sensing, 99:1–14.
-
(2015)
IEEE Transactions on Geoscience and Remote Sensing
, vol.99
, pp. 1-14
-
-
Romero, A.1
Gatta, C.2
Camps-Valls, G.3
-
41
-
-
78049408551
-
Evaluation of pooling operations in convolutional architectures for object recognition
-
15-18 September, Thessaloniki Greece
-
Scherer, D., A. Muller, and S. Behnke, 2010. Evaluation of pooling operations in convolutional architectures for object recognition, Proceedings of the 20th International Conference on Artificial Neural Networks (ICANN), 15-18 September, Thessaloniki Greece, pp. 92–101.
-
(2010)
Proceedings of the 20Th International Conference on Artificial Neural Networks (ICANN)
, pp. 92-101
-
-
Scherer, D.1
Muller, A.2
Behnke, S.3
-
42
-
-
84879853539
-
Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4-D patient data
-
Shin, H., M.R. Orton, D.J. Collins, S.J. Doran, and M.O. Leach, 2013. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4-D patient data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1930–1943.
-
(2013)
IEEE Transactions on Pattern Analysis and Machine Intelligence
, vol.35
, Issue.8
, pp. 1930-1943
-
-
Shin, H.1
Orton, M.R.2
Collins, D.J.3
Doran, S.J.4
Leach, M.O.5
-
43
-
-
84945900998
-
Best practices for convolutional neural networks applied to visual document analysis
-
03-06 August
-
Simard, P.Y., D. Steinkraus, and J.C. Platt, 2003. Best practices for convolutional neural networks applied to visual document analysis, Proceedings of the International Conference on Document Analysis and Recognition, 03-06 August, pp. 958–963.
-
(2003)
Proceedings of the International Conference on Document Analysis and Recognition
, pp. 958-963
-
-
Simard, P.Y.1
Steinkraus, D.2
Platt, J.C.3
-
44
-
-
85083953063
-
Very deep convolutional networks for large-scale image recognition
-
07-09 May, San Diego, California
-
Simonyan, K., and A. Zisserman, 2015. Very deep convolutional networks for large-scale image recognition, Proceedings of the International Conference on Learning Representations, 07-09 May, San Diego, California.
-
(2015)
Proceedings of the International Conference on Learning Representations
-
-
Simonyan, K.1
Zisserman, A.2
-
45
-
-
84868030003
-
Operational utilization of aerial multispectral remote sensing during oil spill response
-
Svejkovsky, J., W. Lehr, J. Muskat, G. Graettinger, and J. Mullin, 2012. Operational utilization of aerial multispectral remote sensing during oil spill response, Photogrammetric Engineering & Remote Sensing, 14:1089–1102.
-
(2012)
Photogrammetric Engineering & Remote Sensing
, vol.14
, pp. 1089-1102
-
-
Svejkovsky, J.1
Lehr, W.2
Muskat, J.3
Graettinger, G.4
Mullin, J.5
-
46
-
-
56449089103
-
Extracting and composing robust features with denoising autoencoders
-
Vincent, P., H. Larochelle, Y. Bengio, and P.-A. Manzagol, 2008. Extracting and composing robust features with denoising autoencoders, Proceedings of the ACM International Conference on Machine Learning, 2008, pp. 1096–1103.
-
(2008)
Proceedings of the ACM International Conference on Machine Learning
, pp. 1096-1103
-
-
Vincent, P.1
Larochelle, H.2
Bengio, Y.3
Manzagol, P.-A.4
-
47
-
-
79551480483
-
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
-
Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, 11(6):3371–3408.
-
(2010)
Journal of Machine Learning Research
, vol.11
, Issue.6
, pp. 3371-3408
-
-
Vincent, P.1
Larochelle, H.2
Lajoie, I.3
Bengio, Y.4
Manzagol, P.5
-
48
-
-
84905898342
-
Spectral-spectral classification of hyperspectral image based on discriminant analysis
-
Yuan, H., Y. Tang, Y. Lu, L. Yang, and H. Luo, 2014. Spectral-spectral classification of hyperspectral image based on discriminant analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2035–2043.
-
(2014)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, vol.7
, Issue.6
, pp. 2035-2043
-
-
Yuan, H.1
Tang, Y.2
Lu, Y.3
Yang, L.4
Luo, H.5
-
49
-
-
84938536972
-
Learning hierarchical spectral-spectral features for hyperspectral image classification
-
Zhou, Y., and Y. Wei, 2015. Learning hierarchical spectral-spectral features for hyperspectral image classification, IEEE Transactions on Cybernetics, 46(7):1667–1678.
-
(2015)
IEEE Transactions on Cybernetics
, vol.46
, Issue.7
, pp. 1667-1678
-
-
Zhou, Y.1
Wei, Y.2
-
50
-
-
84908032942
-
Saliency-guided unsupervised feature learning for scene classification
-
Zhang, F., B. Du, and L. Zhang, 2014. Saliency-guided unsupervised feature learning for scene classification, IEEE Transactions on Geoscience and Remote Sensing, 53:2175–2184.
-
(2014)
IEEE Transactions on Geoscience and Remote Sensing
, vol.53
, pp. 2175-2184
-
-
Zhang, F.1
Du, B.2
Zhang, L.3
|